HIV-1 Supplement
Jack Collora
2023-07-04
HIV infected cells
HIV infected cells were identified by mapping transcriptome to HIV references (HXB2, Autologous, and Clade B) and ATAC-seq profiles to HIV references (HXB2, Autologous). A subset of reads were individually blasted to confirm they do map specifically to HIV, and then cells were annotated. A minimum of two reads was required to declare something HIV infected. See 20230626AnnotateHIV_2.R for exact processing.
HIV infected cells cluster membership and UMAPs (Fig A - B)
results$HIV_type<-case_when(results$HIV_reads_ATAC>1 & results$HIV_reads_RNA>1 ~"HIV RNA+ and HIV DNA+",
results$HIV_reads_ATAC>1 ~ "HIV DNA+", results$HIV_reads_RNA>1 ~ "HIV RNA+",
T ~ "HIV not detected")
umapcords<-data.frame(results@reductions$umap@cell.embeddings)
umapcords$HIV<-results$HIV_type
BottleRocket2 = c("#FAD510", "#CB2314", "#273046", "#354823", "#1E1E1E")
colmap<-c("HIV not detected"="grey62", "HIV RNA+ and HIV DNA+"="#CB2314","HIV DNA+"="#354823","HIV RNA+" = "#FAD510")
ggplot(umapcords, aes(x=UMAP_1, y=UMAP_2, color="HIV not detected"))+geom_point()+
geom_point(data=umapcords[umapcords$HIV!="HIV not detected",], aes(x=UMAP_1, y=UMAP_2, color=HIV),size=2)+
scale_color_manual(values = colmap)+theme_classic()
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig2/A.pdf",width = 16, height = 9)
ggplot(umapcords, aes(x=UMAP_1, y=UMAP_2, color="HIV not detected"))+geom_point()+
geom_point(data=umapcords[umapcords$HIV!="HIV not detected",], aes(x=UMAP_1, y=UMAP_2, color=HIV),size=2)+
scale_color_manual(values = colmap)+theme_classic()
dev.off()## png
## 2meta<-results@meta.data
meta<-group_by(meta, HIV_type, merged_clusters, .drop=FALSE)%>%summarise(n=n())%>%mutate(freq=n/sum(n))## `summarise()` has grouped output by 'HIV_type'. You can override using the
## `.groups` argument.ggplot(meta, aes(x=merged_clusters, y=freq, fill=HIV_type), )+geom_bar(stat="identity", position=position_dodge(width = 1))+scale_fill_manual(values = colmap)+theme_classic()+xlab("Cluster")+ylab("proportion of cells")+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3))
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig2/B.pdf",width = 16, height = 9)
ggplot(meta, aes(x=merged_clusters, y=freq, fill=HIV_type), )+geom_bar(stat="identity", position=position_dodge(width = 1))+scale_fill_manual(values = colmap)+theme_classic()+xlab("Cluster")+ylab("proportion of cells")+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3))
dev.off()## png
## 2#have to simulate because its too resource intensive
#no difference in proportions for clusters across the three groups
fisher.test(cbind(meta$n[meta$HIV_type=="HIV not detected"],
meta$n[meta$HIV_type=="HIV RNA+ and HIV DNA+"],
meta$n[meta$HIV_type=="HIV DNA+"],
meta$n[meta$HIV_type=="HIV RNA+"]), simulate.p.value = TRUE, B = 1e6)##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 1e+06 replicates)
##
## data: cbind(meta$n[meta$HIV_type == "HIV not detected"], meta$n[meta$HIV_type == "HIV RNA+ and HIV DNA+"], meta$n[meta$HIV_type == "HIV DNA+"], meta$n[meta$HIV_type == "HIV RNA+"])
## p-value = 0.1441
## alternative hypothesis: two.sided# its very close with everything pooled but still not sig
fisher.test(rbind(meta$n[meta$HIV_type=="HIV not detected"],
colSums(rbind(meta$n[meta$HIV_type=="HIV RNA+ and HIV DNA+"],
meta$n[meta$HIV_type=="HIV DNA+"],
meta$n[meta$HIV_type=="HIV RNA+"]))), simulate.p.value = TRUE, B = 1e6)##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 1e+06 replicates)
##
## data: rbind(meta$n[meta$HIV_type == "HIV not detected"], colSums(rbind(meta$n[meta$HIV_type == "HIV RNA+ and HIV DNA+"], meta$n[meta$HIV_type == "HIV DNA+"], meta$n[meta$HIV_type == "HIV RNA+"])))
## p-value = 0.05912
## alternative hypothesis: two.sidedHIV_counts<-rbind(meta$n[meta$HIV_type=="HIV not detected"],
colSums(rbind(meta$n[meta$HIV_type=="HIV RNA+ and HIV DNA+"],
meta$n[meta$HIV_type=="HIV DNA+"],
meta$n[meta$HIV_type=="HIV RNA+"])))
#not sig even if we drop clusters that dont have any HIV
fisher.test(HIV_counts[,HIV_counts[2,]!=0], simulate.p.value = TRUE, B = 1e6)##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 1e+06 replicates)
##
## data: HIV_counts[, HIV_counts[2, ] != 0]
## p-value = 0.1462
## alternative hypothesis: two.sidedHIV infected cells treatment conditions (Fig C - D)
results$HIV_bool<-case_when(results$HIV_reads_ATAC>1 | results$HIV_reads_RNA>1 ~TRUE, T ~ FALSE)
results$HIV_bool<-factor(results$HIV_bool, levels = c(TRUE, FALSE))
Timepoint<-table(results$HIV_bool, results$Timepoint)
colnames(Timepoint)<-c("0 Months", "3 Months")
rownames(Timepoint)<-c("HIV_positive","HIV_negative")
to_plot<-data.frame(Timepoint/rowSums(Timepoint))
colnames(to_plot)<-c("HIV","Time","Prop")
colmap<-c("HIV_negative"="grey62", "HIV_positive"="#CB2314")
ggplot(to_plot, aes(x=Time, y=Prop, fill=HIV))+geom_bar(stat="identity", position=position_dodge(width = 1))+scale_fill_manual(values = colmap)+theme_classic()+xlab("Cluster")+ylab("proportion of cells")+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3))
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig2/C.pdf",width = 16, height = 9)
ggplot(to_plot, aes(x=Time, y=Prop, fill=HIV))+geom_bar(stat="identity", position=position_dodge(width = 1))+scale_fill_manual(values = colmap)+theme_classic()+xlab("Cluster")+ylab("proportion of cells")+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3))
dev.off()## png
## 2#no significant difference
chisq.test(Timepoint)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: Timepoint
## X-squared = 1.1475, df = 1, p-value = 0.2841Treatment<-table(results$HIV_bool, results$Treatment)
rownames(Treatment)<-c("HIV_positive","HIV_negative")
to_plot<-data.frame(Treatment/rowSums(Treatment))
colnames(to_plot)<-c("HIV","Treatment","Prop")
colmap<-c("HIV_negative"="grey62", "HIV_positive"="#CB2314")
ggplot(to_plot, aes(x=Treatment, y=Prop, fill=HIV))+geom_bar(stat="identity", position=position_dodge(width = 1))+scale_fill_manual(values = colmap)+theme_classic()+xlab("Cluster")+ylab("proportion of cells")+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3))
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig2/D.pdf",width = 16, height = 9)
ggplot(to_plot, aes(x=Treatment, y=Prop, fill=HIV))+geom_bar(stat="identity", position=position_dodge(width = 1))+scale_fill_manual(values = colmap)+theme_classic()+xlab("Cluster")+ylab("proportion of cells")+theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3))
dev.off()## png
## 2#significantly different across all
chisq.test(Treatment)##
## Pearson's Chi-squared test
##
## data: Treatment
## X-squared = 6.6932, df = 2, p-value = 0.0352#narrowed down to just Naltrexone
chisq.test(table(results$Treatment=="Naltrexone", results$HIV_bool))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(results$Treatment == "Naltrexone", results$HIV_bool)
## X-squared = 4.9462, df = 1, p-value = 0.02615HIV infected cells differential expression (RNA, Fig E - H)
#subset to just within T cell annotations
DefaultAssay(results)<-"RNA"
results$keep<-!is.na(results$CD4anno)
results.sub<-subset(results, keep==TRUE)
Idents(results.sub)<-results.sub$HIV_reads_ATAC>1
marks_ATAC.sub<-FindMarkers(results.sub, TRUE, FALSE)
Idents(results.sub)<-results.sub$HIV_reads_RNA>1
marks_RNA.sub<-FindMarkers(results.sub, TRUE, FALSE)
Idents(results.sub)<-(results.sub$HIV_reads_RNA>1)|(results.sub$HIV_reads_ATAC>1)
marks_either.sub<-FindMarkers(results.sub, TRUE, FALSE)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=marks_ATAC.sub, caption = htmltools::tags$caption( style = 'caption-side: top; text-align: center; color:black; font-size:200% ;','HIV DNA+ cells vs all T cells'))DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=marks_RNA.sub, caption = htmltools::tags$caption( style = 'caption-side: top; text-align: center; color:black; font-size:200% ;','HIV RNA+ cells vs all T cells'))DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=marks_either.sub, caption = htmltools::tags$caption( style = 'caption-side: top; text-align: center; color:black; font-size:200% ;','HIV DNA+ or HIV RNA+ cells vs all T cells'))toPlot<-c("PTPN13","TAF5L", "ZNF195","TRAV8-2")
Idents(results.sub)<-factor(Idents(results.sub), levels = c("TRUE","FALSE"))
VlnPlot(results.sub, features = toPlot, pt.size = 0, ncol = 4)
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig2/E_H.pdf",width = 16, height = 9)
VlnPlot(results.sub, features = toPlot, pt.size = 0, ncol = 4)
dev.off()## png
## 2HIV infected cells differential expression (GSEA) FIG I-L
m_df_H<- msigdbr(species = "Homo sapiens", category = "H")
m_df_H<- rbind(msigdbr(species = "Homo sapiens", category = "C2"), m_df_H)
m_df_H<- rbind(msigdbr(species = "Homo sapiens", category = "C7"), m_df_H)
fgsea_sets<- m_df_H %>% split(x = .$gene_symbol, f = .$gs_name)
DefaultAssay(results)<-"RNA"
results.sub<-subset(results, keep==TRUE)
Idents(results.sub)<-(results.sub$HIV_reads_RNA>1)|(results.sub$HIV_reads_ATAC>1)
#had to use 0.1 to ensure that we filter genes expressed in a single HIV infected cell and no/few other cells, which have insane p values but are functionally uninformative. While in theory GSEA is robust to this, in this case its not going to be reliable.
marks<-FindMarkers(results.sub, TRUE, FALSE, logfc.threshold = -Inf, min.pct = 0.1, min.diff.pct = -Inf)
GSEAres<-GSEA(marks, genesets = fgsea_sets)## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 18 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.GSEAres<-GSEATable(GSEAres, gmt = fgsea_sets, name = "HIVinfected" )## [1] "start ranking"
## [1] "done ranking"GSEAres<-subset(GSEAres,padj<0.05 & NES>0)
DT::datatable(rownames=TRUE, filter="top", class='cell-border stripe', extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print')), data=GSEAres, caption = htmltools::tags$caption( style = 'caption-side: top; text-align: center; color:black; font-size:200% ;','HIV DNA+ or HIV RNA+ cells vs all T cells'))toplot<-c("GSE11057_NAIVE_VS_CENT_MEMORY_CD4_TCELL_DN","ZHENG_BOUND_BY_FOXP3","GSE11057_EFF_MEM_VS_CENT_MEM_CD4_TCELL_UP","GSE14699_DELETIONAL_TOLERANCE_VS_ACTIVATED_CD8_TCELL_UP")
for(i in toplot){
print(GSEAEnrichmentPlotComparison(PathwayName = i, GSEACompOut = GSEAres, returnplot = "BOTH"))
}
pdf("~/gibbs/DOGMAMORPH/Ranalysis/Scripts/Figure Notebooks/rawFigs/fig2/I_L.pdf",width = 16, height = 9)
for(i in toplot){
print(GSEAEnrichmentPlotComparison(PathwayName = i, GSEACompOut = GSEAres, returnplot = "BOTH"))
}
dev.off()## png
## 2devtools::session_info()## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.2.0 (2022-04-22)
## os Red Hat Enterprise Linux 8.8 (Ootpa)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype C
## tz Etc/UTC
## date 2023-07-20
## pandoc 3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)
##
## - Packages -------------------------------------------------------------------
## package * version date (UTC) lib source
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## scattermore 0.8 2022-02-14 [1] CRAN (R 4.2.0)
## sctransform 0.3.5 2022-09-21 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.2.0)
## Seurat * 4.3.0 2022-11-18 [1] CRAN (R 4.2.0)
## SeuratObject * 4.1.3 2022-11-07 [1] CRAN (R 4.2.0)
## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.0)
## Signac * 1.9.0 2022-12-08 [1] CRAN (R 4.2.0)
## sp 1.6-0 2023-01-19 [1] CRAN (R 4.2.0)
## spatstat.data 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.explore 3.1-0 2023-03-14 [1] CRAN (R 4.2.0)
## spatstat.geom 3.1-0 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.random 3.1-4 2023-03-13 [1] CRAN (R 4.2.0)
## spatstat.sparse 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.utils 3.0-2 2023-03-11 [1] CRAN (R 4.2.0)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## survival 3.5-5 2023-03-12 [1] CRAN (R 4.2.0)
## tensor 1.5 2012-05-05 [2] CRAN (R 4.2.0)
## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] CRAN (R 4.2.0)
## vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0)
## vipor 0.4.5 2017-03-22 [2] CRAN (R 4.2.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.2.0)
## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.2.0)
## XVector 0.38.0 2022-11-01 [1] Bioconductor
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
## zlibbioc 1.44.0 2022-11-01 [1] Bioconductor
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
##
## [1] /gpfs/gibbs/project/ya-chi_ho/jac369/R/4.2
## [2] /vast/palmer/apps/avx2/software/R/4.2.0-foss-2020b/lib64/R/library
##
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